Determining a quality score for a content item

ABSTRACT

Systems and methods for determining a quality score or a user engagement level for a content item are provided. The quality score is based on a recentness score of the content item, an affinity score between two users and a popularity score of the content item. The user engagement level for the content item is based on user interactions with the content item, associated times of the user interactions, and interaction types of the user interactions. The user engagement level for the content item is stored in association with the content item.

CROSS REFERENCE TO RELATED APPLICATIONS

This application claims priority under 35 U.S.C. §119(e) and the benefitof U.S. Provisional Application No. 61/707,863, filed Sep. 28, 2012, andentitled, “QUALITY SCORE FOR POSTS,” and U.S. Provisional ApplicationNo. 61/707,869, filed Sep. 28, 2012, and entitled, “DETERMINING A USERENGAGEMENT LEVEL FOR A CONTENT ITEM,” the entire disclosures of whichare incorporated herein by reference.

BACKGROUND

The subject technology generally relates to social networking servicesand, in particular, relates to determining a quality score for a contentitem in a social networking service.

Certain web-based applications provide information in the form of postsby a variety of users. These posts are broadcasted by populatingstreams. The posts are typically presented in reverse chronologicalorder on the streams, but such an arrangement of posts may notprioritize the most informative or useful information when presented toa reader of the posts.

SUMMARY

In some innovative aspects, the disclosed subject matter can be embodiedin a method. The method comprises receiving a content item authored by afirst user to be displayed in a stream corresponding to a second user ina web-based application, where the received content item has anassociated time stamp. A recentness score of the content item isdetermined based on the time stamp. An affinity score representing anaffinity of the first user to the second user in the web-basedapplication is also determined. A popularity score of the content itemis determined based on user interactions with the content item. Aquality score of the content item is generated based on the recentnessscore of the content item and a combination of the affinity score andthe popularity score of the content item.

These and other embodiments can comprise one or more of the followingfeatures. The recentness score of the content item may be determinedbased on a half-life decay function applied to a duration calculated asa current time minus a time corresponding to the time stamp. Thehalf-life decay function calculates a number of half-life decays bydividing the calculated duration by a predetermined half-life duration.The time stamp associated with the content item corresponds to a mostrecent user interaction with the content item. The user interactionswith the content item may comprise one or more of updating the contentitem, commenting on the content item, re-sharing the content item, orendorsing the comment. The affinity of the first user and the seconduser in the web-based application may be determined based on at leastone of a number of and a quality of communication sessions between thefirst user and the second user. The communication sessions comprise oneor more of electronic messages, text chat sessions, audio chat sessions,or video chat sessions, each of the communication sessions having acorresponding quality from which the determination of the affinity scoreis based. Each of the user interactions with the content item is of atype comprising viewing the content item, selecting the content item,re-sharing the content item, commenting on the content item, orendorsing the content item, and wherein each of interactions comprisesan associated time. Generating the quality score of the content itembased on the combination of the affinity score and the popularity scoreof the content item may comprise adjusting the affinity score of thecontent item by a popularity score factor, adjusting the popularityscore of the content item by an affinity score factor, and utilizing ahigher of the adjusted affinity score and the adjusted popularity scoreas a factor in generating the quality score.

In some innovative aspects, the disclosed subject matter relates to acomputer-readable medium encoded with executable instructions. Theinstructions include code for receiving indicia of one or more userinteractions with a content item. Each user interaction in the one ormore user interactions has an associated time and an interaction type.The instructions include code for writing, to a first memory, a log ofthe one or more user interactions with the content item. Theinstructions include code for determining a user engagement level forthe content item based on the associated times and the interaction typesfor the one or more user interactions in the log. The instructionsinclude code for storing the user engagement level for the content itemin association with the content item.

These and other embodiments can include one or more of the followingfeatures. The interaction type includes one or more of: a user viewingthe content item, the user selecting the content item, the userre-sharing the content item, the user commenting on the content item, orthe user endorsing the content item. Determining the user engagementlevel for the content item includes determining a total count of userinteractions in the log. Determining the user engagement level for thecontent item includes determining a total interaction rate as the totalcount divided by a time period since the content item was posted.Determining the user engagement level for the content item includesdetermining a recent count of user interactions in the one or more userinteractions. The recent count corresponds to user interactions takingplace within a predetermined time period before a current time.Determining the user engagement level for the content item includesdetermining a recent interaction rate as the recent count divided by thepredetermined time period. Determining the user engagement level for thecontent item includes determining the user engagement level for thecontent item based on the total interaction rate and the recentinteraction rate. The total count is weighted based on the interactiontypes of the user interactions in the total count and the recent countis weighted based on the interaction types of the user interactions inthe recent count. The user engagement level is determined based on aratio of the recent interaction rate to the total interaction rate. Theinstructions also include code for determining, based on the log, one ormore counts of the user interactions with the content item. Each of theone or more counts corresponds to a time bucket, each time bucketcorresponding to a predetermined time period. The instructions alsoinclude code for storing the one or more counts in association with thecontent item. The one or more counts are weighted based on theinteraction types of the user interactions. The user engagement level isfurther determined based on a ratio of a count of user interactions to anumber of users viewing a stream including the content item. Theinstructions also include code for providing the content item to astream for a viewing user. The instructions also include code fordetermining that the user engagement level exceeds a threshold userengagement level. The instructions also include code for modifying aranking of the content item in the stream based on the user engagementlevel exceeding the threshold user engagement level.

In some innovative aspects, the disclosed subject matter relates to asystem. The system includes one or more processors and a memory. Thememory includes instructions executable by the one or more processors.The instructions include code for receiving indicia of one or more userinteractions with a content item. Each user interaction in the one ormore user interactions having an associated time and an interactiontype. The instructions include code for determining a user engagementlevel for the content item based on the one or more user interactions,the associated times, and the interaction types. The instructionsinclude code for providing the content item to a stream for a viewinguser. The instructions include code for determining that the userengagement level exceeds a threshold user engagement level. Theinstructions include code for modifying a ranking of the content item inthe stream based on the user engagement level exceeding the thresholduser engagement level.

Advantageously, the subject technology improves the user experience whencontent items provided for web-based application streams are presentedin an order influenced by certain characteristics of the content items.By modifying the quality scores of the content items based on suchfactors as freshness, importance, relevance, or popularity, the order inwhich the content items are presented in a stream may be adjusted. Suchadjustments may prioritize content items that are determined to be ofhigher interest to a user.

Advantageously, the subject technology allows for a user engagementlevel for a content item in a social networking service to bedetermined. When presenting a stream to a viewing user of the socialnetworking service, content items having a higher user engagement levelmay be placed closer to the beginning of the stream or otherwisevisually indicated (e.g., highlighted, underlined, or placed inside abox) to bring the user's attention to the content items and increase aprobability that the user will engage with one of the content items.

It is understood that other configurations of the subject technologywill become readily apparent to those skilled in the art from thefollowing detailed description, where various configurations of thesubject technology are shown and described by way of illustration. Aswill be realized, the subject technology is capable of other anddifferent configurations and its several details are capable ofmodification in various other respects, all without departing from thescope of the subject technology. Accordingly, the drawings and detaileddescription are to be regarded as illustrative in nature and not asrestrictive.

BRIEF DESCRIPTION OF THE DRAWINGS

Features of the subject technology are set forth in the appended claims.However, for purpose of explanation, several aspects of the disclosedsubject matter are set forth in the following figures.

FIG. 1 illustrates an example of a system for determining a userengagement level for a content item in a social networking service.

FIG. 2 illustrates an example of the data repository of FIG. 1.

FIG. 3 illustrates an example data structure representing a userinteraction.

FIG. 4 illustrates an example of the server of FIG. 1.

FIG. 5 illustrates an example process by which a user engagement levelfor a content item may be stored.

FIG. 6 illustrates an example process by which indicia of one or moreuser interactions with a content item may be received.

FIG. 7 illustrates an example process by which a user engagement levelfor a content item may be determined.

FIG. 8 illustrates an example process by which a ranking of a contentitem in a stream may be modified.

FIG. 9 illustrates an example of the determine user engagement levelmodule of FIG. 4.

FIG. 10 illustrates an example method for calculating quality scores forcontent items.

FIG. 11 conceptually illustrates an example electronic system with whichsome implementations of the subject technology are implemented.

DETAILED DESCRIPTION

The detailed description set forth below is intended as a description ofvarious configurations of the subject technology and is not intended torepresent the only configurations in which the subject technology may bepracticed. The appended drawings are incorporated herein and constitutea part of the detailed description. The detailed description includesspecific details for the purpose of providing a thorough understandingof the subject technology. However, it will be apparent that the subjecttechnology is not limited to the specific details set forth herein andmay be practiced without these specific details. In some instances,structures and components are shown in block diagram form in order toavoid obscuring the concepts of the subject technology.

A new approach for determining content items that are likely to induceuser engagement may be desirable, for example, for use in arrangingcontent items in a stream of a social networking service. The subjecttechnology provides techniques for determining a user engagement levelfor a content item in a social networking service. According to someaspects, a server of a social networking service receives indicia of oneor more user interactions with a content item in a social networkingservice. Each user interaction in the one or more user interactions hasan associated time and an interaction type. The interaction type cancorrespond to a passive interaction type or an active interaction type.Passive interaction types include, for example, the user viewing (e.g.,pausing scrolling in a stream for at least a threshold time period,e.g., 2 or 3 seconds) or selecting the content item. Active interactiontypes include, for example, the user re-sharing the content item,commenting on the content item, or endorsing the content item. Activeinteraction types can also include interaction types that are notvisible to other users of the social networking service, for example,selection (e.g., click) of a uniform resource locator in a content item,selection of the content item (e.g., on a mobile device), selection ofan image in the content item, or expanding the content item or a commenton the content item. The server determines a user engagement level forthe content item based on the one or more user interactions, theirassociated times, and their interaction types. The server provides theuser engagement level for the content item to a memory (e.g., a localmemory of the server or a data repository external to the server) forstorage in association with the content item.

Some aspects of the subject technology provide techniques forcalculating quality scores for content items provided in a stream on aweb-based application. For example, a quality scores may be calculatedfor social content items that are streamed on a social networkingservice. According to some aspects, a server of a web-based applicationreceives a content item authored by a first user (e.g., a social post)to be displayed in a stream corresponding to a second user in aweb-based application, where the received content item has an associatedtime stamp. The server determines a recentness score of the content itembased on the time stamp. The server may also determine an affinity scorerepresenting an affinity of the first user to the second user in theweb-based application. Furthermore, the server may determine apopularity score of the content item based on user interactions with thecontent item. The server may then generate a quality score of thecontent item based on the recentness score of the content item and acombination of the affinity score and the popularity score of thecontent item.

Some aspects of the subject technology include storing information aboutusers of a social networking service. For example, information may bestored indicating that a user interacted with a content item. A userabout whom information is stored has the option of removing suchinformation from storage at the social networking service. Furthermore,information about user interactions having a passive interaction type isanonymized, i.e., stored without any personal identifiable informationof the associated user. The user affirmatively provides permission forinformation to be stored about him/her or can opt out of havinginformation about him/her stored.

FIG. 1 illustrates an example of a system 100 for determining a userengagement level for a content item in a social networking service. Asshown, the system 100 includes a data repository 110 and a server 120.The data repository 110 and the server 120 communicate with one anotherand with and a client computing device 130 via a network 140. Thenetwork 140 may include the Internet, an intranet, a local area network,a wide area network, a wired network, a wireless network, or a virtualprivate network (VPN). While only one data repository 110, server 120,and client computing device 130 are illustrated, the subject technologymay be implemented in conjunction with any number of data repositories110, servers 120, or client computing devices 130. In some aspects, asingle machine may implement the functions of two or more of the datarepository 110, the server 120, or the client computing device 130.

The data repository 110 stores social content item(s) (e.g., postedcontent item(s)) associated with the social networking service. Oneexample of the data repository 110 is described in more detail inconjunction with FIG. 2 below.

The server 120 includes one or more modules for facilitating userinteraction with the social networking service via a browser or aspecial purpose application executing on the client computing device 130or for processing data stored in the data repository 110. The server 120may be implemented as a single machine with a single processor, amulti-processor machine, or a server farm including multiple machineswith multiple processors. One example of the server 120 is described inmore detail in conjunction with FIG. 4 below.

The client computing device 130 may be a laptop computer, a desktopcomputer, a mobile phone, a personal digital assistant (PDA), a tabletcomputer, a netbook, a television with one or more processors embeddedtherein or coupled thereto, a physical machine, or a virtual machine.The client computing device 130 may include one or more of a keyboard, amouse, a display, or a touch screen. The client computing device 130 mayalso include a browser configured to display webpages, for example awebpage of the social networking service. Alternatively, the clientcomputing device 130 may include a special-purpose application (e.g., amobile phone or tablet computer application) for accessing the socialnetworking service.

FIG. 2 illustrates an example of the data repository 110 of FIG. 1. Asshown, the data repository 110 includes a central processing unit (CPU)202, a network interface 204, and a memory 206. The CPU 202 includes oneor more processors. The CPU 202 is configured to execute computerinstructions that are stored in a computer-readable medium, for example,the memory 206. The network interface 204 is configured to allow thedata repository 110 to transmit and receive data in a network, e.g.,network 140 of FIG. 1. The network interface 204 may include one or morenetwork interface controllers (NICs). The memory 206 stores data and/orinstructions. The memory 206 may be one or more of a cache unit, astorage unit, an internal memory unit, or an external memory unit. Asillustrated, the memory 206 includes a content item 208. While thememory 206 is illustrated as including a single content item 208, thememory 206 may include multiple content items.

The content item 208 corresponds to a content item stored within asocial networking service, for example, a text post, a geographiccheck-in, an image, an album of images, a video, an external link,information about an event, etc. As shown, the content item 208 includesa user interaction log 210, a time table 214, and a user engagementlevel 220.

The user interaction log 210 stores a set of user interactions 212.1-nwith the content item 208. The set of user interactions 212.1-n caninclude active and passive user interactions. The active userinteractions and the passive user interactions in the set of userinteractions 212.1-n are anonymized, i.e., stored without any personalidentifiable information of the associated user. One example of a datastructure for representing a user interaction (e.g., user interaction212.1) is described below in conjunction with FIG. 3.

The time table 214 is created based on the user interaction log 210, forexample, by operation of the server 120. The time table includesmultiple time buckets 216.1-n. Each time bucket corresponds to apredetermined time period. The predetermined time periods can be fixed.For example, one time bucket 216.1 could correspond to 9:00 AM-9:10 AMon Sep. 1, 2012; another time bucket 216.2 could correspond to 9:10AM-9:20 AM on Sep. 1, 2012; and another time bucket 216.3 couldcorrespond to 9:20 AM-9:30 AM on Sep. 1, 2012. Alternatively, thepredetermined time periods can be relative to the current time orrelative to a time when the content item 208 was posted to the socialnetworking service. For example, one time bucket 216.1 can correspond tothe last 15 minutes and another time bucket 216.2 can correspond to15-30 minutes ago. In another example, one time bucket 216.1 cancorrespond to the first hour after the content item 208 was posted andanother time bucket 216.2 can correspond to the second hour after thecontent item 208 was posted.

Each time bucket 216.k is associated with a user interaction count218.k. The user interaction count 218.k corresponds to a count of userinteractions with the content item 208 (e.g., from the user interactionlog 210) having a time within the time bucket 216.k. The userinteraction count 218.k can be weighted based on the interaction typesof the user interactions. For example, active user interactions have ahigher weight than passive user interactions. In some aspects,re-sharing has a higher weight than commenting, which has a higherweight than endorsing. Alternatively, the user interaction count may notbe weighted. Each time bucket 216.k is also associated with a userinteraction rate. The user interaction rate corresponds to a weightednumber of active user interactions divided by weighted number of passiveuser interactions or a number of active user interactions divided by anumber of passive user interactions.

The user engagement level 220 corresponds to a degree to which usersengage (e.g., interact) with the content item 208. The user engagementlevel 220 may be determined based on information in the time table 214.Example techniques for determining the user engagement level 220 aredescribed below, for example, in conjunction with FIG. 5 and FIG. 7. Insome aspects, the user engagement level 220 corresponds to engagement ofusers in general with the content item 208, and may not be personalizedfor an individual user's engagement with a content item. For example, aspecific user may engage with a family photograph posted by the specificuser's mother, while other users of the social networking service, whoare not relatives of the specific user, may not be as interested in thefamily photograph. In these circumstances, the family photograph mayhave a low user engagement level 220, even though the family photographwas very engaging for the specific user.

As illustrated, a single data repository 110 stores the user interactionlog 210, the time table 214, and the user engagement level 220. However,in some aspects, all three of the user interaction log 210, the timetable 214, and the user engagement level 220 can reside in differentdata repositories, or two could reside on the same data repository whileone resides on a separate data repository. Alternatively, one or more ofthe user interaction log 210, the time table 214, and the userengagement level 220 can reside on the server 120.

FIG. 3 illustrates an example data structure representing a userinteraction 212.1.

As shown, the user interaction 212.1 includes a time 302 and aninteraction type 304. The time 302 corresponds to a time when the userinteraction took place (e.g., Sep. 1, 2012, at 9:16:32 AM). Theinteraction type 304 corresponds to the type of user interaction, forexample, viewing, selecting, re-sharing, commenting on, or endorsing theassociated content item.

FIG. 4 illustrates an example of the server 120 of FIG. 1. As shown, theserver 120 includes a central processing unit (CPU) 402, a networkinterface 404, and a memory 406. The CPU 402 includes one or moreprocessors. The CPU 402 is configured to execute computer instructionsthat are stored in a computer-readable medium, for example, the memory406. The network interface 404 is configured to allow the server totransmit and receive data in a network, e.g., network 140 of FIG. 1. Thenetwork interface 404 may include one or more network interfacecontrollers (NICs). The memory 406 stores data and/or instructions. Thememory 406 may be one or more of a cache unit, a storage unit, aninternal memory unit, or an external memory unit. As illustrated, thememory 406 includes a receive user interaction module 408, a determineuser engagement level module 410, a create time table module 412, and aprovide stream module 414.

The receive user interaction module 408 is configured to receive indiciaof one or more user interactions (e.g., user interactions 212.1-n) witha content item (e.g., content item 208) in a social networking service.In some aspects, the receive user interaction module writes the indiciaof the one or more user interactions to a log (e.g., user interactionlog 210), and creates a time table (e.g., time table 214) associatingtime buckets with user interaction counts based on the log.Alternatively, the time table may be created via operation of the createtime table module 412. One example of the operation of the receive userinteraction module 408 is described below in conjunction with FIG. 6.

The determine user engagement level module 410 is configured todetermine a user engagement level (e.g., user engagement level 220) fora content item (e.g., content item 208) based on one or more userinteractions, the associated times, and the interaction types. The userengagement level may be higher if there are a large number of recentuser interactions or the recent user interactions include activeinteractions. One example of the operation of the determine userengagement level module 410 is described below in conjunction with FIG.7 and a block diagram of one example implementation of the determineuser engagement level module 410 is described below in conjunction withFIG. 9.

The create time table module 412 is configured to create a time table(e.g., time table 214) based on a log of user interactions (e.g., userinteraction log) for a content item (e.g., content item 208) bydetermining a user interaction count (e.g., user interaction count218.k) for each time bucket (e.g., time bucket 216.k).

The provide stream module 414 is configured to provide a streamincluding multiple content items to a viewing user of the socialnetworking service. In some examples, the stream is arrangedchronologically based on a time when the content items were posted.Alternatively, a ranking of a content item in the stream may be modifiedfrom the default (e.g., chronological) ranking based on the userengagement level (e.g., user engagement level 220) for the content itemexceeding a threshold user engagement level or based on other factors orother criteria. One example of the operation of the provide streammodule 414 is described below in conjunction with FIG. 8.

FIG. 5 illustrates an example process 500 by which a user engagementlevel for a content item may be stored.

The process 500 begins at step 510, where a server (e.g., server 120)receives indicia of one or more user interactions (e.g., userinteractions 212.1-n) with a content item (e.g., content item 208) in asocial networking service. Each user interaction in the one or more userinteractions has an associated time (e.g., time 302) and an interactiontype (e.g., interaction type 304). In some examples, step 510 is carriedout by the receive user interaction module 408. One example of carryingout the step 510 is described below in conjunction with FIG. 6.

In step 520, the server determines a user engagement level (e.g., userengagement level 220) for the content item based on the one or more userinteractions, the associated time, and the interaction types. In someexamples, step 520 is carried out by the determine user engagementmodule 410. One example of carrying out the step 520 is described belowin conjunction with FIG. 7.

In step 530, the server provides the user engagement level for thecontent item to a memory (e.g., memory 206 of data repository 110) forstorage in association with the content item. After step 530, theprocess 500 ends.

FIG. 6 illustrates an example process 600 by which indicia of one ormore user interactions (e.g., user interactions 212.1-n) with a contentitem (e.g., content item 208) may be received.

The process 600 begins at step 610, where a server (e.g., server 120,via operation of the receive user interaction module 408) receives, fromone or more client computing devices (e.g., client computing device 130)indicia of one or more user interactions with the content item.

In step 620, the server writes, to a log (e.g., user interaction log210) the indicia of the one or more user interactions with the contentitem.

In step 630, the server (e.g., via operation of the create time tablemodule 410) determines, based on the log, one or more counts (e.g., userinteraction counts 218.1-n) of the user interactions with the contentitem. Each of the one or more counts corresponds to a time bucket (e.g.,time buckets 216.1-n). Each time bucket corresponds to a predeterminedtime period. The one or more counts may be weighted based on theinteraction types of the user interaction. For example, active userinteractions have a higher weight than passive user interactions. Insome aspects, re-sharing has a higher weight than commenting, which hasa higher weight than endorsing. Alternatively, the user interactioncount may not be weighted.

In step 640, the server stores the one or more counts in associationwith the content item. After step 640, the process 600 ends.

FIG. 7 illustrates an example process 700 by which a user engagementlevel (e.g., user engagement level 220) for a content item (e.g.,content item 208) may be determined.

The process 700 begins at step 710, where a server (e.g., server 120,via operation of determine user engagement level module 410) determinesa total count of user interactions in one or more user interactions witha content item. For example, the total count can correspond to all ofthe user interactions in a user interaction log (e.g., user interactionlog 210) or all of the user interactions in all time buckets (e.g., timebuckets 216.1-n) of a time table (e.g., time table 214).

In step 720, the server determines a total interaction rate as the totalcount divided by a time period since the content item was posted. Forexample, if there were 30 interactions with the content item and thecontent item was posted 3 hours ago, the total interaction rate is 30interactions divided by 3 hours or 10 interactions per hour.

In step 730, the server determines a recent count of user interactionsin the one or more user interactions with the content item. The recentcount corresponds to user interactions taking place within apredetermined time period (e.g., 10 minutes, the most recent time bucket216.k, or the most recent m time buckets, where m is any positiveinteger) before a current time.

In step 740, the server determines a recent interaction rate as therecent count divided by the predetermined time period. For example, if 4user interactions took place within the last 10 minutes (⅙ hours), therecent interaction rate corresponds to 4 interactions divided by ⅙ hoursor 24 interactions per hour.

In some aspects, the total count and the recent count correspond to anumber of user interactions with the content item during the associatedtime period. In some aspects, the total count and the recent count areweighted based on the interaction types of the user interactions in theassociated time period. For example, active user interactions have ahigher weight than passive user interactions. In some aspects,re-sharing has a higher weight than commenting, which has a higherweight than endorsing. Alternatively, the user interaction count may notbe weighted.

In step 750, the server determines a user engagement level for thecontent item based on a total interaction rate and the recentinteraction rate. The user engagement level may correspond to or bedetermined based on a ratio of the recent interaction rate to the totalinteraction rate. The user engagement level may be further determinedbased on a ratio of a count of user interactions to a number of usersviewing a stream or a page including the content item. For example, acontent item presented to 50 users and interacted with by 10 users maybe as engaging for users as a content item presented to 60 users andinteracted with by 12 users (as 10/50=12/60). However, a content itempresented to 50 users and interacted with by 11 users may be moreengaging than a content item presented to 60 users and interacted withby 12 users (as 11/50>12/60). After step 750, the process 700 ends.

As the user engagement level for the content item may be based on thetotal interaction rate and the recent interaction rate, the userengagement level for the content item may modify with time, as more orfewer users of the social networking service interact with the contentitem. For example, a content item may receive multiple user interactionsduring a time 1-2 hours after the content item is posted, and may have ahigh user engagement level during this time. However, one month afterthe content item is posted, fewer or no users may interact with thecontent item, and the user engagement level of the content item maydecrease.

In some aspects, a content item may be considered “viral” or exciting ifthe user engagement level for the content item or a ratio of the recentinteraction rate to the total interaction rate exceeds a threshold ratio(e.g., 1.25, 1.5, or 2). In such circumstances, the content item islikely to be interesting for other users of the social networkingservice who have permission to view the content item. As a result, thecontent item may be visually indicated (e.g., placed closer to thebeginning of the stream, highlighted, underlined, etc.). In someaspects, viral content item(s) are detected in real-time or nearreal-time after the content item(s) become viral, so that the viralcontent item(s) can be shared with other users while they are stillviral (in some cases, a content item may remain viral for a few minutesor a few hours). As used herein, the phrase “real-time” encompasses itsplain and ordinary meaning including, but not limited to, a calculationor determination that is delayed only by the processing speed ofcomputer(s) carrying out the calculation and is not intentionallydelayed. Depending on the processing speed of the computer(s) and theamount of computation involved, a real-time calculation or determinationmay take on the order of magnitude of one second, one minute, one hour,etc.

FIG. 8 illustrates an example process 800 by which a ranking of acontent item in a stream may be modified.

The process 800 begins at step 810, where a server (e.g., server 120,via operation of provide stream module 414) provides a content item(e.g., content item 208) to a stream for a viewing user of a socialnetworking service.

In step 820, the server determines whether a user engagement level(e.g., user engagement level 220) for the content item exceeds athreshold user engagement level. If so, the process 800 continues tostep 830. If not, the process 800 continues to step 840.

In step 830, if the user engagement level for the content item exceedsthe threshold user engagement level, the server modifies a ranking ofthe content item in the stream, for example, by moving the content itemcloser to a beginning (e.g., a top or left side) of the stream than adefault (e.g., chronological) ranking of the content item. In someimplementations, the ranking of a content item in the stream isdetermined based on a score of the content item, and the server modifiesthe score in order to modify the ranking. For example, if there arethree content items with scores 80, 82, and 85, and the content itemwith score 80 is increased to a score of 84, the content item with score80 will move from being presented at an end of the other two contentitems to in between the other two content items. After step 830, theprocess 800 ends.

In step 840, if the user engagement level for the content item does notexceed the threshold user engagement level, the server foregoesmodifying the ranking of the content item in the stream, leaving thecontent item in the default ranking of the content item. After step 840,the process 800 ends.

Techniques for determining a user engagement level for a content item ina social networking service in a single iteration are described above.However, in some aspects, the user engagement level for the content itemcan be determined in multiple iterations, and the most engaging contentitems in a set of content items can be selected in multiple iterations.For example, in a first iteration, all content items that do not haveany endorsements, re-shares, or comments or have less than a firstthreshold number (e.g., 2 or 3) of endorsements, re-shares, or commentscan be filtered out. In a second iteration, a user engagement rate forthe remaining content items that were not filtered out can bedetermined. A second threshold number (e.g., 3 or 4) of the remainingcontent items having the highest user engagement levels can be selectedfor visual indication (e.g., highlighting, underlining, or changing of aposition or ranking in a stream).

Other iterative approaches can also be used in conjunction with thesubject technology. For example, a first iteration can filter outcontent items that do not have any comments. A second iteration canfilter out content items that do not have any re-shares or endorsements.A third iteration can determine the user engagement rates for theremaining content items and select a predetermined number (e.g., 3 or 4)of the remaining content items for visual indication (e.g.,highlighting, underlining, or changing of a position or ranking in astream) based on the determined user engagement levels.

FIG. 9 illustrates an example of the determine user engagement levelmodule 410 of FIG. 4. As shown, the determine user engagement levelmodule 410 includes a content item reception module 902, a durationcalculation module 904, an affinity score module 906, a popularity scoremodule 908, and a quality score module 910. These modules, which are incommunication with one another, process information retrieved from datarepository 110 in order to calculate quality scores for content items(e.g., content item 208). For example, when a first user authors acontent item that is to be displayed in a stream corresponding to asecond user, the content item is received by content item receptionmodule 902. The received content item may include a time stamp. Durationcalculation module 904 calculates a length of time that has elapsedsince the time of the content item's time stamp. The calculation isperformed as the current time minus the time indicated in the time stampof the content item. A recentness score is determined based on thecalculated length of time. For example, the recentness score may becalculated based on a time-decay function (e.g., half-life function), asdescribed in further detail below.

An affinity score of the content item is calculated by affinity scoremodule 906. Affinity score module 906 is configured to calculate anaffinity score for a pair of users in the web-based application. Thecalculated affinity score represents whether a contact is a closecontact of the user (e.g., a social contact with whom the usercommunicates frequently). For example, the affinity score may bedetermined based on a number and quality of communication sessions(e.g., electronic messages, text, audio, or video chat sessions,telephone calls, etc.) between the pair of users. In some aspects, theaffinity score may be normalized to a probability value, where a scoreabove a predefined value may represent a close relationship between thepair of users (e.g., friends and family, or a content item directed by afirst user to a second user).

A popularity score of the content item is calculated by popularity scoremodule 908 based on one or more user interactions with the content item,the associated times of the interactions, and the interaction types. Thepopularity score may be higher if there are a large number of recentuser interactions or the recent user interactions include activeinteractions. For example, a total count of user interactions may bedetermined for a content item. In some aspects, the popularity score isalso taken into consideration a rate of user interactions within arecent fixed period of time (e.g., the last five hours). The popularityscore of the content item may be determined based a combination of thetotal interaction rate and the recent interaction rate.

Once the recentness, affinity, and popularity scores have beendetermined, quality score module 910 may take the combination of thethree scores to generate a quality score. For example, quality scoremodule 910 may generate a quality score by factoring the recentnessscore with a function of the affinity and popularity scores. In someaspects, the function of the affinity and popularity scores is used todetermine if either of the scores is particularly strong, and to put ahigher emphasis on the stronger score. Thus, a content item havingeither a high affinity score or a high popularity score may produce ahigher factoring value to be used to calculate the quality score (asopposed to an intermediate affinity score and an intermediate popularityscores). Once a quality score has been calculated by quality scoremodule 910, the content item may be provided with the associated qualityfor populating a stream based on the quality score. Additional functionsmay be applied to the quality score to take into consideration othercharacteristics of the content item when determining the priority of thepresentation of the content item in the stream.

FIG. 10 illustrates an example process for calculating quality scoresfor content items. The process 1000 begins at step 1002, where a contentitem authored by a first user to be displayed in a stream correspondingto a second user in a web-based application is received. The receivedcontent item includes an associated time stamp. The content item may beone of a variety of content items entered by a user of a web-basedapplication. For example, the content item may include media such astext, a picture, an audio file, a video file, a hyperlink to a webpage,etc. The content item may also include a combination of two or moremedia. In some aspects, the content item may be shared with a limitedgroup of users (e.g., the author may share the content item with anindividual contact, or a group of contacts). Alternatively, the contentitem may be shared publicly.

In step 1004, a recentness score of the content item is determined basedon the time stamp of the content item. To determine the recentnessscore, a length of time that has elapsed since the time of the contentitem's time stamp is calculated (e.g., the current time minus the timeindicated in the time stamp of the content item). The recentness scoremay be determined based on the calculated length of time. In someaspects, the recentness score may be calculated based on a time-decayfunction (e.g., half-life function). For example, a predeterminedhalf-life of a specified period (e.g., one day) of time may be applied.Thus, if a recentness score is calculated as a value in the range of 0to 1 with one being the most recent, then a content item that is exactlyone day old will have a value of 0.5, as determined by the half-life. Acontent item that is two days old will have a value of 0.25, and so on.While the example utilizes a half-life of one day, the predeterminedhalf-life may be set to be any value of time. The purpose of calculatinga recentness score is to provide a preference for content items thathave been authored more recently over those that were authored lessrecently.

In some implementations, the time stamp of the content item may beupdated based on user interactions with the content item. For example, auser viewing the content item may select the content item, re-share thecontent item, comment on the content item, or endorse the content item.When one of the above-described user interactions is performed on thecontent item, the time stamp of the content item may be set to the timethat the user interaction is performed. As a result of the newly settime stamp, the recentness score of the content item may be increased.

An affinity score representing an affinity of the first user to thesecond user in the web-based application is determined in step 1006. Insome aspects, the calculations are performed on a periodic basis (e.g.,daily, weekly, or monthly). The calculated affinity score representswhether the contact is a close contact of the user (e.g., a socialcontact with whom the user communicates frequently). For example, theaffinity score may be determined based on a number and quality ofcommunication sessions (e.g., electronic messages, text, audio, or videochat sessions, telephone calls, etc.) between the pair of users. As thenumber of communication sessions shared between a pair of usersincreases, the affinity score also increases. Each type of communicationsession may also have different quality characteristics such thatcertain types of communication sessions may increase the affinity scoremore than others. For example, a message directed from a first user to asecond user may increase the affinity score more than a message that isbroadcasted by the first user to a group of users including the seconduser.

The effects of each communication session on the affinity score may beadjusted according to a time-decay function. For example, the effect ofa communication session between two users may be reduced by a half whena certain predefined period of time has elapsed since the communicationsession. In some aspects, the affinity score is a normalized probabilityvalue representing the likelihood that the second user will interactwith a content item given that the content item was authored by thefirst user.

A popularity score of the content item is determined based on userinteractions with the content item in step 1008. The popularity score ofthe content item may be calculated based on one or more userinteractions and the associated times. The popularity score may bedetermined to be higher if there are a large number of recent userinteractions or the recent user interactions include activeinteractions. In some aspects, a total count of user interactions may bedetermined for a content item. The total count may be divided by a timeperiod since the content item was posted to generate a rate (e.g.,number of user interactions per unit of time). Thus, if there were 30interactions with the content item and the content item was posted 3hours ago, then the interaction rate is calculated to be 10 interactionsper hour. In some aspects, the popularity score may also take intoconsideration a rate of user interactions within a recent fixed periodof time (e.g., the last five hours). The total count and the recentcount, once calculated, may be weighted based on the interaction typesof the user interactions in the associated time period. For example,active user interactions have a higher weight than passive userinteractions. In some aspects, re-sharing has a higher weight thancommenting, which has a higher weight than endorsing. Alternatively, theuser interaction count may not be weighted.

The popularity score may also correspond to a ratio of the recentinteraction rate to the total interaction rate, where a higher ratio ofrecent to total interactions produces a higher popularity score than alower ratio. Additionally, the score may be determined based on a ratioof a count of user interactions to a number of users viewing a stream ora page including the content item. For example, a content item that ispresented to 50 users and interacted with by 10 users may be as engagingfor users as a content item presented to 300 users and interacted withby 60 users, as both ratio of presentation to interaction are theidentical. However, a content item presented to 50 users and interactedwith by 11 users may be determined to be more engaging than a contentitem presented to 60 users and interacted with by 12 users, as theformer ratio is larger than the latter.

As the popularity of the content item may be based on the totalinteraction rate and the recent interaction rate, the popularity scorefor the content item may change with time, as more or fewer users of theweb-based application interact with the content item. For example, acontent item may receive multiple user interactions during a period inthe first two hours after the content item is posted, and may have ahigh popularity score during this time. However, when fewer usersinteract with the content item at another period of time, the popularityscore of the content item may decrease. In some aspects, the popularityscore may also be normalized to be a probability value. Thus, popularityscore is representative of a probability that any user will interactwith a particular content item.

Once the recentness, affinity, and popularity scores have beendetermined, a quality score of the content item may be generated in step1010. The quality score may be generated based on the recentness scoreof the content item and a combination of the affinity score and thepopularity score of the content item. In some aspects, the recentnessscore may be factored with a function of the affinity and popularityscores. A linear function may be applied to the affinity and popularityscores to emphasize a strength in either the affinity score or thepopularity score. The linear function, for example, may take the higherof the following two scores:score1=affinity score×f(popularity score); andscore2=popularity score×f(affinity score),where f(popularity score) is a factor based on the popularity of thecontent item, and where f(affinity score) is a factor based on theaffinity of the content item. The affinity score and the popularityscore are normalized to produce factors normalized to a fixed range ofvalues. That factor is then multiplied by the score of the othercharacteristic (e.g., the affinity score is multiplied by the popularityfactor, and the popularity score is multiplied by the affinity factor).Using factors that are within a normalized a range ensures that theaffinity or popularity score is not unduly minimized by a lowcomplementary factor.

Additionally, taking the maximum of score1 and score2, as shown in thisexample, magnifies the effect of either a high affinity score or a highpopularity score when calculating the quality score. Thus, a contentitem having either a high affinity score or a high popularity score mayproduce a higher factoring value to be used to calculate the qualityscore than a content item having two intermediate values. Once a qualityscore has been calculated, the content item may be provided with theassociated quality for populating a stream based on the quality score.

In some implementations, a topicality of the content item may also befactored into the calculation of the quality score of the content item.Topicality represents how well a content item matches the user'sinterests. For example, if a user has indicated a particular interest infootball (e.g., through profile preferences), a content item that isrelated to football may result in a topicality factor that increases thequality score of the content item. The user interest may be determinedbased on information that is explicitly or implicitly provided by theuser (e.g., preferences specified in the user's profile, userinteraction with other content items, etc.). The user affirmativelyprovides permission for such information to be stored and may opt out ofhaving such information available when quality scores are calculated.For example, the user may choose to not have this information stored bythe web-based application.

In some aspects, the quality scoring of content items may be processediteratively to reduce the amount of processing required. Each of thescoring factors—recentness, affinity, popularity, and topicality, inthis case—may be processed individually and sequentially. For example, arecentness score of content items may be determined first. Oncedetermined, a subset of the content items that have a recentness scoreabove a predetermined threshold is selected for further processing.Affinity scores may be determined for the subset of content items next,followed by a determination of popularity scores and then topicalityscores. With each score determination, only the content items thatsatisfy a predetermined threshold value are maintained for subsequentscore determinations. Content items that have scored low on any one ofthe scoring factors are filtered out, thereby reducing the number ofcontent items that are to be processed for a next scoring factor. Insome aspects, the order of the iterative process may be altered.Furthermore, when two factors are required to calculate a single score(e.g., the affinity score is adjusted by a popularity score factor orthe popularity score is adjusted by an affinity score factor), theiterative process may determine the scores in parallel before filteringout the content items that do not satisfy a threshold value.

Some aspects of the subject technology include storing information aboutusers of a web-based application. For example, information indicatingthat a user interacted with a content item may be stored. An option toremove such information from storage or to not store the informationaltogether may be provided to the user by the web-based application.Furthermore, information about user interactions having a passiveinteraction type may be anonymized (e.g., stored without any personalidentifiable information of the associated user).

FIG. 11 conceptually illustrates an electronic system 1100 with whichsome implementations of the subject technology are implemented. Forexample, one or more of the data repository 110, the server 120, or theclient computing device 130 may be implemented using the arrangement ofthe electronic system 1100. The electronic system 1100 can be a computer(e.g., a mobile phone, PDA), or any other sort of electronic device.Such an electronic system includes various types of computer readablemedia and interfaces for various other types of computer readable media.Electronic system 1100 includes a bus 1105, processing unit(s) 1110, asystem memory 1115, a read-only memory 1120, a permanent storage device1125, an input device interface 1130, an output device interface 1135,and a network interface 1140.

The bus 1105 collectively represents all system, peripheral, and chipsetbuses that communicatively connect the numerous internal devices of theelectronic system 1100. For instance, the bus 1105 communicativelyconnects the processing unit(s) 1110 with the read-only memory 1120, thesystem memory 1115, and the permanent storage device 1125.

From these various memory units, the processing unit(s) 1110 retrievesinstructions to execute and data to process in order to execute theprocesses of the subject technology. The processing unit(s) can be asingle processor or a multi-core processor in different implementations.

The read-only-memory (ROM) 1120 stores static data and instructions thatare needed by the processing unit(s) 1110 and other modules of theelectronic system. The permanent storage device 1125, on the other hand,is a read-and-write memory device. This device is a non-volatile memoryunit that stores instructions and data even when the electronic system1100 is off. Some implementations of the subject technology use amass-storage device (for example a magnetic or optical disk and itscorresponding disk drive) as the permanent storage device 1125.

Other implementations use a removable storage device (for example afloppy disk, flash drive, and its corresponding disk drive) as thepermanent storage device 1125. Like the permanent storage device 1125,the system memory 1115 is a read-and-write memory device. However,unlike storage device 1125, the system memory 1115 is a volatileread-and-write memory, such a random access memory. The system memory1115 stores some of the instructions and data that the processor needsat runtime. In some implementations, the processes of the subjecttechnology are stored in the system memory 1115, the permanent storagedevice 1125, or the read-only memory 1120. For example, the variousmemory units include instructions for determining a quality score for acontent item in a social networking service in accordance with someimplementations. From these various memory units, the processing unit(s)1110 retrieves instructions to execute and data to process in order toexecute the processes of some implementations.

The bus 1105 also connects to the input and output device interfaces1130 and 1135. The input device interface 1130 enables the user tocommunicate information and select commands to the electronic system.Input devices used with input device interface 1130 include, forexample, alphanumeric keyboards and pointing devices (also called“cursor control devices”). Output device interfaces 1135 enables, forexample, the display of images generated by the electronic system 1100.Output devices used with output device interface 1135 include, forexample, printers and display devices, for example cathode ray tubes(CRT) or liquid crystal displays (LCD). Some implementations includedevices for example a touch screen that functions as both input andoutput devices.

Finally, as shown in FIG. 11, bus 1105 also couples electronic system1100 to a network (not shown) through a network interface 1140. In thismanner, the electronic system 1100 can be a part of a network ofcomputers (for example a local area network (“LAN”), a wide area network(“WAN”), or an Intranet, or a network of networks, for example theInternet. Any or all components of electronic system 1100 can be used inconjunction with the subject technology.

The above-described features and applications can be implemented assoftware processes that are specified as a set of instructions recordedon a computer readable storage medium (also referred to as computerreadable medium). When these instructions are executed by one or moreprocessing unit(s) (e.g., one or more processors, cores of processors,or other processing units), they cause the processing unit(s) to performthe actions indicated in the instructions. Examples of computer readablemedia include, but are not limited to, CD-ROMs, flash drives, RAM chips,hard drives, EPROMs, etc. The computer readable media does not includecarrier waves and electronic signals passing wirelessly or over wiredconnections.

In this specification, the term “software” is meant to include firmwareresiding in read-only memory or applications stored in magnetic storageor flash storage, for example, a solid-state drive, which can be readinto memory for processing by a processor. Also, in someimplementations, multiple software technologies can be implemented assub-parts of a larger program while remaining distinct softwaretechnologies. In some implementations, multiple software technologiescan also be implemented as separate programs. Finally, any combinationof separate programs that together implement a software technologydescribed here is within the scope of the subject technology. In someimplementations, the software programs, when installed to operate on oneor more electronic systems, define one or more specific machineimplementations that execute and perform the operations of the softwareprograms.

A computer program (also known as a program, software, softwareapplication, script, or code) can be written in any form of programminglanguage, including compiled or interpreted languages, declarative orprocedural languages, and it can be deployed in any form, including as astand alone program or as a module, component, subroutine, object, orother unit suitable for use in a computing environment. A computerprogram may, but need not, correspond to a file in a file system. Aprogram can be stored in a portion of a file that holds other programsor data (e.g., one or more scripts stored in a markup languagedocument), in a single file dedicated to the program in question, or inmultiple coordinated files (e.g., files that store one or more modules,sub programs, or portions of code). A computer program can be deployedto be executed on one computer or on multiple computers that are locatedat one site or distributed across multiple sites and interconnected by acommunication network.

These functions described above can be implemented in digital electroniccircuitry, in computer software, firmware or hardware. The techniquescan be implemented using one or more computer program products.Programmable processors and computers can be included in or packaged asmobile devices. The processes and logic flows can be performed by one ormore programmable processors and by one or more programmable logiccircuitry. General and special purpose computing devices and storagedevices can be interconnected through communication networks.

Some implementations include electronic components, for examplemicroprocessors, storage and memory that store computer programinstructions in a machine-readable or computer-readable medium(alternatively referred to as computer-readable storage media,machine-readable media, or machine-readable storage media). Someexamples of such computer-readable media include RAM, ROM, read-onlycompact discs (CD-ROM), recordable compact discs (CD-R), rewritablecompact discs (CD-RW), read-only digital versatile discs (e.g., DVD-ROM,dual-layer DVD-ROM), a variety of recordable/rewritable DVDs (e.g.,DVD-RAM, DVD-RW, DVD+RW, etc.), flash memory (e.g., SD cards, mini-SDcards, micro-SD cards, etc.), magnetic or solid state hard drives,read-only and recordable Blu-Ray® discs, ultra density optical discs,any other optical or magnetic media, and floppy disks. Thecomputer-readable media can store a computer program that is executableby at least one processing unit and includes sets of instructions forperforming various operations. Examples of computer programs or computercode include machine code, for example is produced by a compiler, andfiles including higher-level code that are executed by a computer, anelectronic component, or a microprocessor using an interpreter.

While the above discussion primarily refers to microprocessor ormulti-core processors that execute software, some implementations areperformed by one or more integrated circuits, for example applicationspecific integrated circuits (ASICs) or field programmable gate arrays(FPGAs). In some implementations, such integrated circuits executeinstructions that are stored on the circuit itself

As used in this specification and any claims of this application, theterms “computer”, “server”, “processor”, and “memory” all refer toelectronic or other technological devices. These terms exclude people orgroups of people. For the purposes of the specification, the termsdisplay or displaying means displaying on an electronic device. As usedin this specification and any claims of this application, the terms“computer readable medium” and “computer readable media” are entirelyrestricted to tangible, physical objects that store information in aform that is readable by a computer. These terms exclude any wirelesssignals, wired download signals, and any other ephemeral signals.

To provide for interaction with a user, implementations of the subjectmatter described in this specification can be implemented on a computerhaving a display device, e.g., a CRT (cathode ray tube) or LCD (liquidcrystal display) monitor, for displaying information to the user and akeyboard and a pointing device, e.g., a mouse or a trackball, by whichthe user can provide input to the computer. Other kinds of devices canbe used to provide for interaction with a user as well; for example,feedback provided to the user can be any form of sensory feedback, e.g.,visual feedback, auditory feedback, or tactile feedback; and input fromthe user can be received in any form, including acoustic, speech, ortactile input. In addition, a computer can interact with a user bysending documents to and receiving documents from a device that is usedby the user; for example, by sending web pages to a web browser on auser's client device in response to requests received from the webbrowser.

The subject matter described in this specification can be implemented ina computing system that includes a back end component, e.g., as a dataserver, or that includes a middleware component, e.g., an applicationserver, or that includes a front end component, e.g., a client computerhaving a graphical user interface or a Web browser through which a usercan interact with an implementation of the subject matter described inthis specification, or any combination of one or more such back end,middleware, or front end components. The components of the system can beinterconnected by any form or medium of digital data communication,e.g., a communication network. Examples of communication networksinclude a local area network (“LAN”) and a wide area network (“WAN”), aninter-network (e.g., the Internet), and peer-to-peer networks (e.g., adhoc peer-to-peer networks).

The computing system can include clients and servers. A client andserver are generally remote from each other and typically interactthrough a communication network. The relationship of client and serverarises by virtue of computer programs running on the respectivecomputers and having a client-server relationship to each other. In someaspects of the disclosed subject matter, a server transmits data (e.g.,an HTML page) to a client device (e.g., for purposes of displaying datato and receiving user input from a user interacting with the clientdevice). Data generated at the client device (e.g., a result of the userinteraction) can be received from the client device at the server.

It is understood that any specific order or hierarchy of steps in theprocesses disclosed is an illustration of example approaches. Based upondesign preferences, it is understood that the specific order orhierarchy of steps in the processes may be rearranged, or that allillustrated steps be performed. Some of the steps may be performedsimultaneously. For example, in certain circumstances, multitasking andparallel processing may be advantageous. Moreover, the separation ofvarious system components illustrated above should not be understood asrequiring such separation, and it should be understood that thedescribed program components and systems can generally be integratedtogether in a single software product or packaged into multiple softwareproducts.

Various modifications to these aspects will be readily apparent, and thegeneric principles defined herein may be applied to other aspects. Thus,the claims are not intended to be limited to the aspects shown herein,but is to be accorded the full scope consistent with the languageclaims, where reference to an element in the singular is not intended tomean “one and only one” unless specifically so stated, but rather “oneor more.” Unless specifically stated otherwise, the term “some” refersto one or more. Pronouns in the masculine (e.g., his) include thefeminine and neuter gender (e.g., her and its) and vice versa. Headingsand subheadings, if any, are used for convenience only and do not limitthe subject technology.

A phrase, for example, an “aspect” does not imply that the aspect isessential to the subject technology or that the aspect applies to allconfigurations of the subject technology. A disclosure relating to anaspect may apply to all configurations, or one or more configurations. Aphrase, for example, an aspect may refer to one or more aspects and viceversa. A phrase, for example, a “configuration” does not imply that suchconfiguration is essential to the subject technology or that suchconfiguration applies to all configurations of the subject technology. Adisclosure relating to a configuration may apply to all configurations,or one or more configurations. A phrase, for example, a configurationmay refer to one or more configurations and vice versa.

What is claimed is:
 1. A computer-implemented method comprising: receiving a content item authored by a first user and that is to be displayed in a stream corresponding to a second user in a web-based application, the received content item associated with a social networking service and having an associated time stamp; determining a recentness score of the content item based on the time stamp; determining an affinity score representing an affinity of the first user to the second user; determining a popularity score of the content item based on user interactions with the content item; and generating a quality score of the content item based on the recentness score of the content item and a combination of the affinity score and the popularity score of the content item, the generating the quality score comprising: adjusting the affinity score of the content item by a popularity score factor; adjusting the popularity score of the content item by an affinity score factor; and utilizing a higher of the adjusted affinity score and the adjusted popularity score as a factor in generating the quality score.
 2. The method of claim 1, further comprising: determining a ranking of the content item in the stream based on the quality score.
 3. The computer-implemented method of claim 1, wherein the recentness score of the content item is determined based on a half-life decay function applied to a duration calculated as a current time minus a time corresponding to the time stamp.
 4. The computer-implemented method of claim 3, wherein the half-life decay function calculates a number of half-life decays by dividing the calculated duration by a predetermined half-life duration.
 5. The computer-implemented method of claim 1, wherein the time stamp associated with the content item corresponds to a most recent user interaction with the content item.
 6. The computer-implemented method of claim 5, wherein the user interactions with the content item comprise one or more of updating the content item, commenting on the content item, re-sharing the content item, or endorsing the content item.
 7. The computer-implemented method of claim 1, wherein the affinity of the first user and the second user is determined based on at least one of a number of or a quality of communication sessions between the first user and the second user.
 8. The computer-implemented method of claim 7, wherein the communication sessions comprise one or more of electronic messages, text chat sessions, audio chat sessions, or video chat sessions, each of the communication sessions having a corresponding quality from which the determination of the affinity score is based.
 9. The computer-implemented method of claim 1, wherein each of the user interactions with the content item is of a type comprising one or more of viewing the content item, selecting the content item, re-sharing the content item, commenting on the content item, or endorsing the content item, and wherein each of user interactions has an associated time.
 10. A non-transitory computer-readable medium comprising instructions which, when executed by one or more computers, cause the one or more computers to implement a method, the method comprising: receiving indicia of one or more user interactions with a content item associated with a social networking service, each user interaction in the one or more user interactions having an associated time and an interaction type; writing a log of the one or more user interactions with the content item; determining a user engagement level for the content item based on the associated times and the interaction types for the one or more user interactions in the log, the determining the user engagement level comprising: determining a total count of user interactions in the log; determining a total interaction rate as the total count divided by a time period since the content item was posted; determining a recent count of user interactions in the one or more user interactions, the recent count corresponding to user interactions taking place within a predetermined time period before a current time; determining a recent interaction rate as the recent count divided by the predetermined time period; determining the user engagement level for the content item based on the total interaction rate and the recent interaction rate; and storing the user engagement level for the content item in association with the content item.
 11. The computer-readable medium of claim 10, wherein the interaction type comprises one or more of: a user viewing the content item, the user selecting the content item, the user re-sharing the content item, the user commenting on the content item, or the user endorsing the content item.
 12. The computer-readable medium of claim 10, wherein the total count is weighted based on the interaction types of the user interactions in the total count and the recent count is weighted based on the interaction types of the user interactions in the recent count.
 13. The computer-readable medium of claim 10, wherein the user engagement level is determined based on a ratio of the recent interaction rate to the total interaction rate.
 14. The computer-readable medium of claim 10, the method further comprising: determining, based on the log, one or more counts of the user interactions with the content item, each of the one or more counts corresponding to a time bucket, each time bucket corresponding to a predetermined time period; and storing the one or more counts in association with the content item.
 15. The computer-readable medium of claim 14, wherein the one or more counts are weighted based on the interaction types of the user interactions.
 16. The computer-readable medium of claim 10, wherein the user engagement level is further determined based on a ratio of a count of user interactions to a number of users viewing a stream comprising the content item.
 17. The computer-readable medium of claim 10, the method further comprising: providing the content item to a stream for a viewing user; determining that the user engagement level exceeds a threshold user engagement level; and modifying a ranking of the content item in the stream based on the user engagement level exceeding the threshold user engagement level.
 18. A system comprising: one or more processors; and a memory comprising instructions which, when executed by the one or more processors, cause the one or more processors to implement a method, the method comprising: receiving indicia of one or more user interactions with a content item associated with a social networking service, each user interaction in the one or more user interactions having an associated time and an interaction type; determining a user engagement level for the content item based on the one or more user interactions, the associated times of the one or more user interactions, and the interaction types of the one or more user interactions, the determining the user engagement level comprising: determining a total interaction rate as a total count of user interactions divided by a time period since the content item was posted; determining a recent interaction rate as a recent count of user interactions divided by a predetermined time period; and determining the user engagement level for the content item based on the total interaction rate and the recent interaction rate; providing the content item to a stream for a viewing user; determining that the user engagement level exceeds a threshold user engagement level; and modifying a ranking of the content item in the stream based on the user engagement level exceeding the threshold user engagement level. 